Overview

Dataset statistics

Number of variables13
Number of observations3142
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory319.2 KiB
Average record size in memory104.0 B

Variable types

Numeric13

Alerts

rural_urban_continuum_code_2003 is highly overall correlated with census_2010_popHigh correlation
census_2010_pop is highly overall correlated with rural_urban_continuum_code_2003High correlation
fips has unique valuesUnique
economic_typology_2015 has 1236 (39.3%) zerosZeros
residual_2010 has 504 (16.0%) zerosZeros
r_international_mig_2011 has 310 (9.9%) zerosZeros

Reproduction

Analysis started2023-01-16 23:46:26.084748
Analysis finished2023-01-16 23:46:39.678050
Duration13.59 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

fips
Real number (ℝ)

Distinct3142
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30383.649
Minimum1001
Maximum56045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2023-01-17T00:46:39.727361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile5093.1
Q118177.5
median29176
Q345080.5
95-th percentile53062.9
Maximum56045
Range55044
Interquartile range (IQR)26903

Descriptive statistics

Standard deviation15162.508
Coefficient of variation (CV)0.49903513
Kurtosis-1.0980535
Mean30383.649
Median Absolute Deviation (MAD)12020
Skewness-0.079661813
Sum95465426
Variance2.2990166 × 108
MonotonicityStrictly increasing
2023-01-17T00:46:39.802602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001 1
 
< 0.1%
39089 1
 
< 0.1%
39093 1
 
< 0.1%
39095 1
 
< 0.1%
39097 1
 
< 0.1%
39099 1
 
< 0.1%
39101 1
 
< 0.1%
39103 1
 
< 0.1%
39105 1
 
< 0.1%
39107 1
 
< 0.1%
Other values (3132) 3132
99.7%
ValueCountFrequency (%)
1001 1
< 0.1%
1003 1
< 0.1%
1005 1
< 0.1%
1007 1
< 0.1%
1009 1
< 0.1%
1011 1
< 0.1%
1013 1
< 0.1%
1015 1
< 0.1%
1017 1
< 0.1%
1019 1
< 0.1%
ValueCountFrequency (%)
56045 1
< 0.1%
56043 1
< 0.1%
56041 1
< 0.1%
56039 1
< 0.1%
56037 1
< 0.1%
56035 1
< 0.1%
56033 1
< 0.1%
56031 1
< 0.1%
56029 1
< 0.1%
56027 1
< 0.1%
Distinct9
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1317632
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2023-01-17T00:46:39.865395image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q37
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6847198
Coefficient of variation (CV)0.52315738
Kurtosis-1.2880286
Mean5.1317632
Median Absolute Deviation (MAD)2
Skewness-0.14270757
Sum16124
Variance7.2077205
MonotonicityNot monotonic
2023-01-17T00:46:39.922235image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
6 608
19.4%
7 449
14.3%
9 438
13.9%
1 414
13.2%
3 350
11.1%
2 325
10.3%
8 235
 
7.5%
4 218
 
6.9%
5 105
 
3.3%
ValueCountFrequency (%)
1 414
13.2%
2 325
10.3%
3 350
11.1%
4 218
 
6.9%
5 105
 
3.3%
6 608
19.4%
7 449
14.3%
8 235
 
7.5%
9 438
13.9%
ValueCountFrequency (%)
9 438
13.9%
8 235
 
7.5%
7 449
14.3%
6 608
19.4%
5 105
 
3.3%
4 218
 
6.9%
3 350
11.1%
2 325
10.3%
1 414
13.2%

economic_typology_2015
Real number (ℝ)

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8084023
Minimum0
Maximum5
Zeros1236
Zeros (%)39.3%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2023-01-17T00:46:39.977605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.819511
Coefficient of variation (CV)1.0061428
Kurtosis-1.304376
Mean1.8084023
Median Absolute Deviation (MAD)1
Skewness0.44504894
Sum5682
Variance3.3106202
MonotonicityNot monotonic
2023-01-17T00:46:40.032136image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1236
39.3%
3 501
15.9%
1 444
 
14.1%
4 407
 
13.0%
5 333
 
10.6%
2 221
 
7.0%
ValueCountFrequency (%)
0 1236
39.3%
1 444
 
14.1%
2 221
 
7.0%
3 501
15.9%
4 407
 
13.0%
5 333
 
10.6%
ValueCountFrequency (%)
5 333
 
10.6%
4 407
 
13.0%
3 501
15.9%
2 221
 
7.0%
1 444
 
14.1%
0 1236
39.3%

census_2010_pop
Real number (ℝ)

Distinct3090
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98262.036
Minimum82
Maximum9818605
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2023-01-17T00:46:40.100319image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum82
5-th percentile2908.7
Q111114.5
median25872
Q366780
95-th percentile422716.05
Maximum9818605
Range9818523
Interquartile range (IQR)55665.5

Descriptive statistics

Standard deviation312946.7
Coefficient of variation (CV)3.184818
Kurtosis345.31339
Mean98262.036
Median Absolute Deviation (MAD)18494
Skewness14.355061
Sum3.0873932 × 108
Variance9.7935637 × 1010
MonotonicityNot monotonic
2023-01-17T00:46:40.179819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21720 3
 
0.1%
8729 2
 
0.1%
8457 2
 
0.1%
17392 2
 
0.1%
12161 2
 
0.1%
4139 2
 
0.1%
2756 2
 
0.1%
4936 2
 
0.1%
6970 2
 
0.1%
17866 2
 
0.1%
Other values (3080) 3121
99.3%
ValueCountFrequency (%)
82 1
< 0.1%
90 1
< 0.1%
286 1
< 0.1%
416 1
< 0.1%
460 1
< 0.1%
478 1
< 0.1%
494 1
< 0.1%
539 1
< 0.1%
614 1
< 0.1%
632 1
< 0.1%
ValueCountFrequency (%)
9818605 1
< 0.1%
5194675 1
< 0.1%
4092459 1
< 0.1%
3817117 1
< 0.1%
3095313 1
< 0.1%
3010232 1
< 0.1%
2504700 1
< 0.1%
2496435 1
< 0.1%
2368139 1
< 0.1%
2230722 1
< 0.1%

residual_2010
Real number (ℝ)

Distinct186
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum-394
Maximum996
Zeros504
Zeros (%)16.0%
Negative1634
Negative (%)52.0%
Memory size24.7 KiB
2023-01-17T00:46:40.255573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-394
5-th percentile-20
Q1-3
median-1
Q31
95-th percentile18
Maximum996
Range1390
Interquartile range (IQR)4

Descriptive statistics

Standard deviation38.294665
Coefficient of variation (CV)nan
Kurtosis255.25466
Mean0
Median Absolute Deviation (MAD)2
Skewness10.241716
Sum0
Variance1466.4814
MonotonicityNot monotonic
2023-01-17T00:46:40.329248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 504
16.0%
-1 452
14.4%
1 314
 
10.0%
-2 297
 
9.5%
-3 181
 
5.8%
2 156
 
5.0%
-4 127
 
4.0%
-5 94
 
3.0%
3 94
 
3.0%
-6 77
 
2.5%
Other values (176) 846
26.9%
ValueCountFrequency (%)
-394 1
< 0.1%
-381 1
< 0.1%
-340 1
< 0.1%
-304 1
< 0.1%
-197 1
< 0.1%
-195 1
< 0.1%
-187 2
0.1%
-185 1
< 0.1%
-175 1
< 0.1%
-167 1
< 0.1%
ValueCountFrequency (%)
996 1
< 0.1%
840 1
< 0.1%
493 1
< 0.1%
462 1
< 0.1%
434 1
< 0.1%
300 1
< 0.1%
267 1
< 0.1%
249 1
< 0.1%
239 1
< 0.1%
236 1
< 0.1%

r_birth_2011
Real number (ℝ)

Distinct3131
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.743215
Minimum0
Maximum32.279402
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2023-01-17T00:46:40.408732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.8406551
Q110.05583
median11.52512
Q313.043651
95-th percentile16.333918
Maximum32.279402
Range32.279402
Interquartile range (IQR)2.9878207

Descriptive statistics

Standard deviation2.7452699
Coefficient of variation (CV)0.23377499
Kurtosis4.6882934
Mean11.743215
Median Absolute Deviation (MAD)1.4918324
Skewness1.1364235
Sum36897.181
Variance7.536507
MonotonicityNot monotonic
2023-01-17T00:46:40.484019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.389671361 3
 
0.1%
9.216589862 3
 
0.1%
11.33263379 2
 
0.1%
10.19757807 2
 
0.1%
11.9760479 2
 
0.1%
13.08803556 2
 
0.1%
11.69590643 2
 
0.1%
10.75268817 2
 
0.1%
11.65135559 2
 
0.1%
11.6 1
 
< 0.1%
Other values (3121) 3121
99.3%
ValueCountFrequency (%)
0 1
< 0.1%
2.574002574 1
< 0.1%
3.450258769 1
< 0.1%
3.669724771 1
< 0.1%
4.287245445 1
< 0.1%
4.436557232 1
< 0.1%
4.532292585 1
< 0.1%
4.587704743 1
< 0.1%
4.878048781 1
< 0.1%
4.938852305 1
< 0.1%
ValueCountFrequency (%)
32.27940204 1
< 0.1%
29.65819357 1
< 0.1%
28.33093821 1
< 0.1%
27.90697674 1
< 0.1%
27.33060717 1
< 0.1%
26.9541779 1
< 0.1%
26.91390729 1
< 0.1%
26.07361963 1
< 0.1%
25.64288828 1
< 0.1%
23.83532544 1
< 0.1%

r_death_2011
Real number (ℝ)

Distinct3136
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.189561
Minimum0
Maximum24.941873
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size24.7 KiB
2023-01-17T00:46:40.560348image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.7507123
Q18.4033068
median10.18449
Q311.905845
95-th percentile14.653768
Maximum24.941873
Range24.941873
Interquartile range (IQR)3.5025377

Descriptive statistics

Standard deviation2.7405084
Coefficient of variation (CV)0.26895254
Kurtosis0.79734996
Mean10.189561
Median Absolute Deviation (MAD)1.7507629
Skewness0.17450652
Sum32015.602
Variance7.5103864
MonotonicityNot monotonic
2023-01-17T00:46:40.638313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3
 
0.1%
12.7388535 2
 
0.1%
8.385744235 2
 
0.1%
8.89920681 2
 
0.1%
10.6856634 2
 
0.1%
9.345454546 1
 
< 0.1%
10.62462914 1
 
< 0.1%
8.688820077 1
 
< 0.1%
12.64545638 1
 
< 0.1%
9.364407735 1
 
< 0.1%
Other values (3126) 3126
99.5%
ValueCountFrequency (%)
0 3
0.1%
0.897585495 1
 
< 0.1%
1.03626943 1
 
< 0.1%
1.593855017 1
 
< 0.1%
1.86770428 1
 
< 0.1%
1.924619086 1
 
< 0.1%
2.042900919 1
 
< 0.1%
2.425222312 1
 
< 0.1%
2.650022665 1
 
< 0.1%
2.697599137 1
 
< 0.1%
ValueCountFrequency (%)
24.94187275 1
< 0.1%
22.33009709 1
< 0.1%
22.09381373 1
< 0.1%
20.97735399 1
< 0.1%
20.73301988 1
< 0.1%
19.9572345 1
< 0.1%
19.67213115 1
< 0.1%
19.51219512 1
< 0.1%
19.45181256 1
< 0.1%
19.43145016 1
< 0.1%

r_international_mig_2011
Real number (ℝ)

Distinct2829
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.85911785
Minimum-2.0429009
Maximum25.836576
Zeros310
Zeros (%)9.9%
Negative590
Negative (%)18.8%
Memory size24.7 KiB
2023-01-17T00:46:40.715047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2.0429009
5-th percentile-0.32727691
Q10
median0.33048308
Q31.0743624
95-th percentile3.9662681
Maximum25.836576
Range27.879477
Interquartile range (IQR)1.0743624

Descriptive statistics

Standard deviation1.6501638
Coefficient of variation (CV)1.9207653
Kurtosis31.127767
Mean0.85911785
Median Absolute Deviation (MAD)0.39120179
Skewness4.16735
Sum2699.3483
Variance2.7230405
MonotonicityNot monotonic
2023-01-17T00:46:40.787147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 310
 
9.9%
0.1241156758 2
 
0.1%
-0.149097957 2
 
0.1%
-0.133040644 2
 
0.1%
0.1077237962 2
 
0.1%
0.0727272727 1
 
< 0.1%
0.3083267493 1
 
< 0.1%
0.2469257741 1
 
< 0.1%
0.276604305 1
 
< 0.1%
0.6894113488 1
 
< 0.1%
Other values (2819) 2819
89.7%
ValueCountFrequency (%)
-2.042900919 1
< 0.1%
-1.520450053 1
< 0.1%
-1.345593182 1
< 0.1%
-1.200819383 1
< 0.1%
-1.169317119 1
< 0.1%
-1.095629391 1
< 0.1%
-1.084468008 1
< 0.1%
-0.982183646 1
< 0.1%
-0.972940103 1
< 0.1%
-0.946521533 1
< 0.1%
ValueCountFrequency (%)
25.83657588 1
< 0.1%
16.37426901 1
< 0.1%
15.23394995 1
< 0.1%
14.40244616 1
< 0.1%
13.61506498 1
< 0.1%
12.68248391 1
< 0.1%
12.292563 1
< 0.1%
12.02764563 1
< 0.1%
11.00270488 1
< 0.1%
10.95461903 1
< 0.1%

r_domestic_mig_2011
Real number (ℝ)

Distinct3129
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.7251387
Minimum-124.54212
Maximum122.90503
Zeros13
Zeros (%)0.4%
Negative1899
Negative (%)60.4%
Memory size24.7 KiB
2023-01-17T00:46:40.861448image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-124.54212
5-th percentile-16.027846
Q1-6.2393706
median-1.8355965
Q32.6504986
95-th percentile12.46373
Maximum122.90503
Range247.44715
Interquartile range (IQR)8.8898692

Descriptive statistics

Standard deviation10.250563
Coefficient of variation (CV)-5.9418775
Kurtosis20.766262
Mean-1.7251387
Median Absolute Deviation (MAD)4.4550004
Skewness0.6187114
Sum-5420.3857
Variance105.07403
MonotonicityNot monotonic
2023-01-17T00:46:40.935442image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
0.4%
-0.468466358 2
 
0.1%
5.945454545 1
 
< 0.1%
-5.032915793 1
 
< 0.1%
-8.619505378 1
 
< 0.1%
-9.659379766 1
 
< 0.1%
-2.286944239 1
 
< 0.1%
-0.706464147 1
 
< 0.1%
2.271590952 1
 
< 0.1%
-3.545874755 1
 
< 0.1%
Other values (3119) 3119
99.3%
ValueCountFrequency (%)
-124.5421245 1
< 0.1%
-63.11463379 1
< 0.1%
-59.15349312 1
< 0.1%
-57.67524401 1
< 0.1%
-53.83265067 1
< 0.1%
-52.00594354 1
< 0.1%
-47.20482922 1
< 0.1%
-43.96085557 1
< 0.1%
-41.35393672 1
< 0.1%
-41.1227154 1
< 0.1%
ValueCountFrequency (%)
122.9050279 1
< 0.1%
84.18386352 1
< 0.1%
72.17789126 1
< 0.1%
70.56721751 1
< 0.1%
66.91449814 1
< 0.1%
60.51873199 1
< 0.1%
60.2335587 1
< 0.1%
46.83195592 1
< 0.1%
46.24115098 1
< 0.1%
45.78904334 1
< 0.1%

r_domestic_mig_2012
Real number (ℝ)

Distinct3129
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.714678
Minimum-100.71708
Maximum119.42091
Zeros13
Zeros (%)0.4%
Negative2034
Negative (%)64.7%
Memory size24.7 KiB
2023-01-17T00:46:41.010558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-100.71708
5-th percentile-19.218931
Q1-7.7527539
median-2.7595665
Q32.3671814
95-th percentile13.207725
Maximum119.42091
Range220.13799
Interquartile range (IQR)10.119935

Descriptive statistics

Standard deviation11.646973
Coefficient of variation (CV)-4.2903699
Kurtosis16.555485
Mean-2.714678
Median Absolute Deviation (MAD)5.0718355
Skewness0.34558173
Sum-8529.5182
Variance135.65197
MonotonicityNot monotonic
2023-01-17T00:46:41.086611image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
0.4%
-2.120890774 2
 
0.1%
-7.13581841 1
 
< 0.1%
-5.945392697 1
 
< 0.1%
-1.682381376 1
 
< 0.1%
-2.795425667 1
 
< 0.1%
-2.824858757 1
 
< 0.1%
-0.068070872 1
 
< 0.1%
-6.25107333 1
 
< 0.1%
-4.352170285 1
 
< 0.1%
Other values (3119) 3119
99.3%
ValueCountFrequency (%)
-100.7170795 1
< 0.1%
-99.44751381 1
< 0.1%
-99.02067465 1
< 0.1%
-83.01886792 1
< 0.1%
-78.24477928 1
< 0.1%
-66.45990253 1
< 0.1%
-60.85706068 1
< 0.1%
-55.11697487 1
< 0.1%
-53.78670788 1
< 0.1%
-51.06986641 1
< 0.1%
ValueCountFrequency (%)
119.4209087 1
< 0.1%
111.5302037 1
< 0.1%
79.66211064 1
< 0.1%
68.06579694 1
< 0.1%
64.60040375 1
< 0.1%
59.52380952 1
< 0.1%
56.39457083 1
< 0.1%
54.86973717 1
< 0.1%
53.89221557 1
< 0.1%
50.89538171 1
< 0.1%

r_domestic_mig_2013
Real number (ℝ)

Distinct3133
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.6947713
Minimum-110.7302
Maximum208.33333
Zeros10
Zeros (%)0.3%
Negative1908
Negative (%)60.7%
Memory size24.7 KiB
2023-01-17T00:46:41.341258image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-110.7302
5-th percentile-17.179779
Q1-6.7467548
median-1.9898425
Q33.1020287
95-th percentile14.285888
Maximum208.33333
Range319.06354
Interquartile range (IQR)9.8487835

Descriptive statistics

Standard deviation11.884555
Coefficient of variation (CV)-7.0124829
Kurtosis44.548306
Mean-1.6947713
Median Absolute Deviation (MAD)4.9258877
Skewness2.1215664
Sum-5324.9713
Variance141.24264
MonotonicityNot monotonic
2023-01-17T00:46:41.416840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10
 
0.3%
-4.121041931 1
 
< 0.1%
1.426166765 1
 
< 0.1%
-2.819290112 1
 
< 0.1%
1.468229692 1
 
< 0.1%
-5.384295499 1
 
< 0.1%
5.611789396 1
 
< 0.1%
-3.884321254 1
 
< 0.1%
-5.943693711 1
 
< 0.1%
3.979762363 1
 
< 0.1%
Other values (3123) 3123
99.4%
ValueCountFrequency (%)
-110.7302023 1
< 0.1%
-99.19070347 1
< 0.1%
-66.86726847 1
< 0.1%
-60.05354752 1
< 0.1%
-57.19785725 1
< 0.1%
-55.78512397 1
< 0.1%
-54.23728814 1
< 0.1%
-49.54530247 1
< 0.1%
-49.30332262 1
< 0.1%
-48.34147596 1
< 0.1%
ValueCountFrequency (%)
208.3333333 1
< 0.1%
136.3004173 1
< 0.1%
88.37836878 1
< 0.1%
83.02808303 1
< 0.1%
71.69473841 1
< 0.1%
59.01639344 1
< 0.1%
58.15876912 1
< 0.1%
55.31279385 1
< 0.1%
49.66442953 1
< 0.1%
47.24760419 1
< 0.1%

r_domestic_mig_2014
Real number (ℝ)

Distinct3122
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.9100998
Minimum-174.35897
Maximum149.97285
Zeros20
Zeros (%)0.6%
Negative1900
Negative (%)60.5%
Memory size24.7 KiB
2023-01-17T00:46:41.492277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-174.35897
5-th percentile-18.246082
Q1-7.2175679
median-2.0866745
Q33.2726858
95-th percentile14.465101
Maximum149.97285
Range324.33182
Interquartile range (IQR)10.490254

Descriptive statistics

Standard deviation11.170276
Coefficient of variation (CV)-5.8480066
Kurtosis32.285646
Mean-1.9100998
Median Absolute Deviation (MAD)5.2108929
Skewness-0.11999588
Sum-6001.5335
Variance124.77507
MonotonicityNot monotonic
2023-01-17T00:46:41.564890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20
 
0.6%
-5.08864084 2
 
0.1%
1.842729429 1
 
< 0.1%
-0.242114762 1
 
< 0.1%
-7.9954889 1
 
< 0.1%
14.26016782 1
 
< 0.1%
-3.099721239 1
 
< 0.1%
-3.643125498 1
 
< 0.1%
3.903062535 1
 
< 0.1%
-6.378424658 1
 
< 0.1%
Other values (3112) 3112
99.0%
ValueCountFrequency (%)
-174.3589744 1
< 0.1%
-76.60498726 1
< 0.1%
-50.52493438 1
< 0.1%
-47.5994513 1
< 0.1%
-46.81211065 1
< 0.1%
-46.31125471 1
< 0.1%
-45.65630945 1
< 0.1%
-43.17548747 1
< 0.1%
-43.14329738 1
< 0.1%
-42.49433171 1
< 0.1%
ValueCountFrequency (%)
149.9728489 1
< 0.1%
77.07279097 1
< 0.1%
67.7180414 1
< 0.1%
56.09353181 1
< 0.1%
55.80570337 1
< 0.1%
52.04508857 1
< 0.1%
48.9274304 1
< 0.1%
47.30617608 1
< 0.1%
42.7480916 1
< 0.1%
41.11083735 1
< 0.1%

r_domestic_mig_2016
Real number (ℝ)

Distinct3134
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.377192
Minimum-124.70956
Maximum208.81928
Zeros8
Zeros (%)0.3%
Negative1822
Negative (%)58.0%
Memory size24.7 KiB
2023-01-17T00:46:41.643495image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-124.70956
5-th percentile-19.951277
Q1-7.4058533
median-1.7541757
Q34.5474314
95-th percentile18.546319
Maximum208.81928
Range333.52885
Interquartile range (IQR)11.953285

Descriptive statistics

Standard deviation13.204112
Coefficient of variation (CV)-9.587706
Kurtosis31.10016
Mean-1.377192
Median Absolute Deviation (MAD)5.9772596
Skewness1.3220777
Sum-4327.1374
Variance174.34858
MonotonicityNot monotonic
2023-01-17T00:46:41.717253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8
 
0.3%
-2.610966057 2
 
0.1%
-3.074478537 1
 
< 0.1%
1.08819262 1
 
< 0.1%
-5.659471536 1
 
< 0.1%
-17.98528269 1
 
< 0.1%
-5.58302587 1
 
< 0.1%
-0.443093095 1
 
< 0.1%
1.920669005 1
 
< 0.1%
1.682993139 1
 
< 0.1%
Other values (3124) 3124
99.4%
ValueCountFrequency (%)
-124.7095642 1
< 0.1%
-78.15163184 1
< 0.1%
-68.647085 1
< 0.1%
-63.04407587 1
< 0.1%
-60.54803439 1
< 0.1%
-60.38066069 1
< 0.1%
-56.08938547 1
< 0.1%
-56.06711684 1
< 0.1%
-55.78635015 1
< 0.1%
-53.89182488 1
< 0.1%
ValueCountFrequency (%)
208.8192825 1
< 0.1%
157.2803079 1
< 0.1%
51.89786059 1
< 0.1%
46.1626813 1
< 0.1%
41.4507772 1
< 0.1%
40.31632811 1
< 0.1%
40.12414099 1
< 0.1%
40.08135097 1
< 0.1%
40.07820137 1
< 0.1%
39.99919841 1
< 0.1%

Interactions

2023-01-17T00:46:38.551583image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:26.289295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:27.216179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:28.178703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:29.072502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:29.977014image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:32.085202image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:32.972736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:33.839400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:34.838316image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:35.699614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:36.567104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:37.503811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:38.619224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:26.365085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:27.289783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:28.246228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:29.140140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:30.047049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:32.151755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:33.036799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:33.906490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:34.903323image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:35.764407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:36.634513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:37.575314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:38.695090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:26.447597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:27.370489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:28.322492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:29.214805image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:30.123005image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:32.225097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:33.108789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:33.979777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:34.975609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:35.835016image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:36.710568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:37.787209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:38.764201image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:26.518182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:27.446600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:28.390370image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:29.282949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:30.193169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:32.293919image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:33.174032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:34.048420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:35.040516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:35.901222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:36.778480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:37.858025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:38.833811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:26.586351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:27.522848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:28.460290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:29.352525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:30.264591image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:32.363889image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:33.241655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:34.116552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:35.107308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:35.968309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:36.847695image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:37.926744image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:38.904803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:26.656549image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:27.598094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:28.530975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:29.424678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:31.526026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:32.434670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:33.310603image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:34.300223image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:35.175301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:36.039243image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:36.918711image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:38.000450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:38.974346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:26.725071image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:27.671350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:28.601367image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:29.494932image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:31.595658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:32.501711image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:33.378187image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:34.369054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:35.242049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:36.105320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:36.988325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:38.071341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:39.041395image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:26.792885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:27.742191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:28.669103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:29.563044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:31.663825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:32.567464image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:33.443507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:34.434418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:35.306377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:36.171678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:37.054948image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:38.139543image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:39.112703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:26.862487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:27.813589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:28.736222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:29.631263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:31.733850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:32.634455image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:33.508252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:34.500525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:35.371828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:36.236659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:37.128527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:38.206975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:39.177802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:26.932238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:27.885655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:28.801066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:29.699058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:31.801646image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:32.701183image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:33.570799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:34.567270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:35.434568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:36.300803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:37.198076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:38.272763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:39.243210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:26.998367image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:27.956590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:28.867355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:29.766401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:31.869907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:32.764869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:33.635197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:34.631670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:35.497605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:36.364206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:37.267147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:38.338930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:39.311365image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:27.070898image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:28.030908image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:28.936274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:29.836249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:31.942092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:32.835429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:33.704190image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:34.700122image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:35.565906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:36.432033image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:37.349207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:38.409226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:39.382538image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:27.143819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:28.104266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:29.004828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:29.906044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:32.013592image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:32.902482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:33.771512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:34.769029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:35.633222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:36.499435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:37.426856image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-17T00:46:38.479059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2023-01-17T00:46:41.790035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
fipsrural_urban_continuum_code_2003economic_typology_2015census_2010_popresidual_2010r_birth_2011r_death_2011r_international_mig_2011r_domestic_mig_2011r_domestic_mig_2012r_domestic_mig_2013r_domestic_mig_2014r_domestic_mig_2016
fips1.0000.0010.017-0.015-0.071-0.0590.015-0.0200.1350.1270.1150.0740.033
rural_urban_continuum_code_20030.0011.0000.172-0.7650.189-0.1820.436-0.296-0.154-0.173-0.156-0.197-0.208
economic_typology_20150.0170.1721.000-0.1170.042-0.187-0.022-0.0170.010-0.0110.0240.0180.034
census_2010_pop-0.015-0.765-0.1171.000-0.3000.226-0.4160.3590.2030.2180.1600.1990.188
residual_2010-0.0710.1890.042-0.3001.0000.093-0.075-0.064-0.013-0.014-0.004-0.013-0.006
r_birth_2011-0.059-0.182-0.1870.2260.0931.000-0.2900.190-0.021-0.015-0.081-0.119-0.168
r_death_20110.0150.436-0.022-0.416-0.075-0.2901.000-0.335-0.111-0.155-0.122-0.120-0.127
r_international_mig_2011-0.020-0.296-0.0170.359-0.0640.190-0.3351.0000.0960.1370.0350.021-0.011
r_domestic_mig_20110.135-0.1540.0100.203-0.013-0.021-0.1110.0961.0000.3710.3620.3370.276
r_domestic_mig_20120.127-0.173-0.0110.218-0.014-0.015-0.1550.1370.3711.0000.4010.3770.256
r_domestic_mig_20130.115-0.1560.0240.160-0.004-0.081-0.1220.0350.3620.4011.0000.4290.338
r_domestic_mig_20140.074-0.1970.0180.199-0.013-0.119-0.1200.0210.3370.3770.4291.0000.471
r_domestic_mig_20160.033-0.2080.0340.188-0.006-0.168-0.127-0.0110.2760.2560.3380.4711.000

Missing values

2023-01-17T00:46:39.497757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-17T00:46:39.620660image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fipsrural_urban_continuum_code_2003economic_typology_2015census_2010_popresidual_2010r_birth_2011r_death_2011r_international_mig_2011r_domestic_mig_2011r_domestic_mig_2012r_domestic_mig_2013r_domestic_mig_2014r_domestic_mig_2016
010012.00.05457111.011.6000009.3454550.0727275.945455-5.971992-4.1210421.8427294.831664
110034.05.018226546.011.8429959.8953120.95761115.68425916.21967421.69181619.61082920.402397
210056.03.0274572.012.10946111.816785-0.1829220.475598-6.457531-7.762540-5.289429-18.890745
310071.00.0229152.011.57514012.1012820.438452-5.436808-4.624328-6.6831900.709944-1.328845
410091.00.057322-6.012.9463359.901162-0.2784160.487228-1.737016-1.128482-2.744366-1.773605
510116.03.0109142.015.68372712.3428151.577653-23.571992-7.612424-8.0359259.051480-2.982346
610136.00.020947-3.013.11067512.6321830.047849-3.684387-8.185670-14.819870-0.540766-7.666650
710153.04.0118572-8.011.72973311.2215860.220197-6.368779-5.158896-5.640650-4.595762-4.356845
810176.00.034215-2.011.76729512.9704350.792312-2.1715213.5810211.670501-4.639145-6.170561
910198.00.025989-2.08.54635011.857099-0.0769944.4656612.0405416.195286-0.7705355.475728
fipsrural_urban_continuum_code_2003economic_typology_2015census_2010_popresidual_2010r_birth_2011r_death_2011r_international_mig_2011r_domestic_mig_2011r_domestic_mig_2012r_domestic_mig_2013r_domestic_mig_2014r_domestic_mig_2016
3132560279.00.024840.07.2376368.443908-0.804182-1.6083632.01572325.886101-22.257552-12.892828
3133560297.05.0282050.010.5133189.2079730.0000006.0680908.3133945.427080-4.5159968.565384
3134560317.00.086671.09.44591611.864993-0.3455826.5660645.509010-1.8349689.493852-13.058419
3135560337.00.029116-5.011.71252910.2056540.2054831.7808526.6354745.3327032.1817572.272461
3136560359.02.0102472.013.1070575.184135-0.293442-15.06333417.810473-39.126435-8.166880-11.285329
3137560375.02.04380623.014.6185475.710370-0.9821841.48469615.777408-6.189066-12.141391-18.664058
3138560397.05.0212944.012.1751354.073987-0.749239-1.8262701.30117618.20706013.795245-1.426071
3139560417.02.0211183.015.4817206.621412-1.095629-17.101346-3.723861-10.107993-14.363345-11.340331
3140560437.00.085330.012.7215979.305613-0.235585-12.603805-2.965951-1.188919-18.698310-14.595877
3141560457.02.072083.011.2970719.902371-0.418410-8.647141-11.1118934.9250690.000000-1.247574